Reducing uncertainty in carbon cycle science of North America: a synthesis program across United

States and Mexico Rodrigo Vargas

Department of Plant and Soil SciencesUniversity of Delaware

CoPIs: Nathaniel BrunsellUniversity of Kansas

Daniel HayesUniversity of Maine

Contact: rvargas@udel.edu

Agroclimatology PD meetingDecember 16-18, 2016San Francisco, CA

Interagency Carbon Cycle Science (FY 2014)(2014-67003-22070)

• Synthesize new existing datasets and models across the United States (U.S.) and Mexico in a consistent analysis framework.

…directed towards improving our understanding of forest and soil carbon dynamics, and the validation of terrestrial ecosystem models.

The specific objectives:a) Harmonize available datasets b) Develop the synthesis approaches for scaling informationc) Develop a to identify knowledge gaps.

Objectives

Biederman, et al (2016) Global Change Biology 22:1867–1879. Villarreal, et al (2016) Journal of Geophysical Research-Biogeosciences 121:494-508. Petrie, et al (2016) Journal of Geophysical Research- Biogeosciences 121:280-294. Reimer, et al (2016) Progress in Oceanography 143 (2016) 1–12 McKinney et al (2015) IEEE 11th International Conference on e-Science: 108-117. Programa de Investigación en Cambio Climático (PICC) (2015) Reporte Mexicano de Cambio Climático. (Mexican Report on Climate Change. Group I: Scientific Bases, Models and Modeling).FAO and ITPS (2015) Status of the World’s Soil Resources (SWSR) – Main Report. Vargas , et al (2015) EOS, 96. doi:10.1029/ 2015EO037893 Reimer, et al (2015) PLoS ONE. 10(4):e0125177Cueva, et al (2015) Journal of Geophysical Research-Biogeosciences 120:737-751. King, et al (2015) Biogeosciences 12:399-414 Milne, et al (2015) “Soil Carbon: science, management and policy for multiple benefits”. CABI. 10-25.Banwart, et al (2014) Carbon Management 5:185-19Hengl, et al. SoilGrids250m: global gridded soil information based on Machine Learning (in review) PlosONE

Vargas R, et al. (in review) Enhancing interoperability to facilitate implementation of REDD+: case study of Mexico. Carbon Management

Publications

Soil carbon across North America

- For decades the USA and Mexico have collected soil organic carbon (SOC) information.

- Can we describe the spatial variability of SOC across North America?

- Can we relate observations with biophysical information to predict SOC?

• Digital soil mapping (predictive soil mapping)

- Computer-assisted production of digital maps of soil properties.

- Use of field and laboratory observational (data and methods) with spatial and non-spatial inference systems.

Digital soil mapping

+ many others

United States MexicoInternational Soil Carbon Network Federal agencies

NRCS N=94778 1938-2010

INEGI Legacy Series 1 N=21153 1969-2001

USGS N=5623 1928-2006

INEGI Legacy Series 2 N=2805 1999-2009

Oak Ridge National Lab N=588 1992-2006

INEGI – National land degradation project N=2472 2008-2012

Other institutions (e.g. Universities, Long Term Ecological Research sites) N=2330 1905-2009

CONAFOR – INFyS N=3061 2009-2011

TOTAL=103319 analyzed samples TOTAL=29491 analyzed samples

NRCS = Natural Resource Conservation ServiceUSGS = United States Geological SurveyINEGI = National Institute for Statistics and GeographyCONAFOR = National Forest Commission

SOC databases

SOC databases

0-30cm n=12,360

SOC database for USA (ISCN)

• Randomized sample from INEGI series 1 & 2

0-30cm n=12,997

SOC database for Mexico (INEGI and CONAFOR)

SOC = f(S,C,O,R,P,A,N)+eSoil- soil type mapsClimate, climatic propertiesOrganisms, land cover and natural vegetationRelief, terrain parameters from DEM`sParent material, geological maps Age, the time factorN, space, relative positione, autocorrelated random spatial variation

Dokuchayev 1883->Jenny 1941->McBratney et al., 2003,-> Grunwald et al., 2011

Conceptual model for SOC variability

Model evaluation(e.g. cross valitacion,

AIC, BIC, Cp)

Variable selection(e.g. linear

model)

Prediction to

new data(e.g. random

forestCubist)

Uncertainty Estimation(e.g. different

models, Global/local)

SOC = f (Soils, Climate, Organisms, Parent material, Age, Space) + error

Digital soil mapping

Guevara and Vargas (in preparation)

Hypothesis driven

Machine learning

*Median

Statistical performance(explained variance)

Unexplained variability

Model r raca RMSEraca r inegi RMSE inegi MX (Pg) US (Pg)

linear 5km 0.45 0.94 0.47 0.23 16.6 ± 2.16e-05 23.16 ± 2.96e-05

rf 5km 0.46 0.95 0.33 0.24 17.4 ±2.68-e05 21.53 ± 3.36e-05

SOC stocks across North America (PRELIMINARY)

29.3Pg for 0-30 cm depth (SSURGO; Bliss et al 2014) for CONUS

14.2 Pg for 0-20 cm depth (+- 3.9 Pg; Murray-Tortarolo et al 2015) for Mexico

RaCA = Rapid Assessment of US Soil Carbon (USDA)INEGI = Series 1& 2

RaCA INEGI

Soil carbon density:CONUS = 2.8 Mg km-2

Mexico = 8.5 Mg km-2

Towards a continental map of SOC for North America

SOC next steps

- - This approach represents a regional baseline estimate of SOC (0-30cm) including variability

- - Useful in future soil sampling planning (i.e. for inventory, SOC monitoring networks) aiming to reduce areas dominated by high variability

- -This approach is reproducible (and semi automated) and can be periodically updated with new data and new covariates (Land use time 1, land use time2 and so on)

Conclusions

Vargas et al (in review)

Stakeholder Scientists

Interoperability

Vargas et al (in review)

Interoperability

Interoperability is a collective effort with the ultimate goal of sharing and using information to produce knowledge and apply knowledge gained, by removing conceptual, technological, organizational, and cultural barriers.

Vargas et al (in review)

Interoperability

Interagency Carbon Cycle Science (FY 2014)(2014-67003-22070)

THANK YOU